Normal and Abnormal Personality Traits are Associated with Marital Satisfaction for both Men and Women: An Actor–Partner Interdependence Model Analysis

Research has demonstrated associations between relationship satisfaction and personality traits. Using the Actor–Partner Interdependence Model, we explored associations between self-reported relationship satisfaction in couples (n = 118) and various measures of normal and abnormal personality, including higher-order dimensions of PE/Extraversion, NE/Neuroticism, Constraint (CON), and their lower-order facets. We also examined gender differences and moderators of associations. Consistent with the Vulnerability Stress Adaptation Model, self- and partner-reported NE and PE were related to satisfaction, and their lower-order traits demonstrated differential associations with satisfaction. Further, abnormal personality traits specific to the interpersonal domain and personality disorder symptoms demonstrated effects. Relationship length emerged as a significant moderator, with associations weakening as relationship duration increased

Overestimating Self-Blame for Stressful Life Events and Adolescents’ Latent Trait Cortisol (LTC): The Moderating Role of Parental Warmth. Journal of Youth and Adolescence

Cognitive interpretations of stressful events impact their implications for physiological stress processes. However, whether such interpretations are related to trait cortisol—an indicator of individual differences in stress physiology—is unknown. In 112 early adolescent girls (M age = 12.39 years), this study examined the association between self-blame estimates for past year events and latent trait cortisol, and whether maternal warmth moderated effects. Overestimating self-blame (versus objective indices) for independent (uncontrollable) events was associated with lower latent trait cortisol, and maternal warmth moderated the effect of self-blame estimates on latent trait cortisol for each dependent (at least partially controllable) and interpersonal events. Implications for understanding the impact of cognitive and interpersonal factors on trait cortisol during early adolescence are discussed

Chronic and episodic interpersonal stress as statistically unique predictors of depression in two samples of emerging adults.

Few studies comprehensively evaluate which types of life stress are most strongly associated with depressive episode onsets, over and above other forms of stress, and comparisons between acute and chronic stress are particularly lacking. Past research implicates major (moderate to severe) stressful life events (SLEs), and to a lesser extent, interpersonal forms of stress; research conflicts on whether dependent or independent SLEs are more potent, but theory favors dependent SLEs. The present study used 5 years of annual diagnostic and life stress interviews of chronic stress and SLEs from 2 separate samples (Sample 1 N = 432; Sample 2 N = 146) transitioning into emerging adulthood; 1 sample also collected early adversity interviews. Multivariate analyses simultaneously examined multiple forms of life stress to test hypotheses that all major SLEs, then particularly interpersonal forms of stress, and then dependent SLEs would contribute unique variance to major depressive episode (MDE) onsets. Person-month survival analysis consistently implicated chronic interpersonal stress and major interpersonal SLEs as statistically unique predictors of risk for MDE onset. In addition, follow-up analyses demonstrated temporal precedence for chronic stress; tested differences by gender; showed that recent chronic stress mediates the relationship between adolescent adversity and later MDE onsets; and revealed interactions of several forms of stress with socioeconomic status (SES). Specifically, as SES declined, there was an increasing role for noninterpersonal chronic stress and noninterpersonal major SLEs, coupled with a decreasing role for interpersonal chronic stress. Implications for future etiological research were discussed

Keeping Data Science Broad: Negotiating the Digital and Data Divide Among Higher Education Institutions

The goal of the “Keeping Data Science Broad” series of webinars and workshops was to garner community input into pathways for keeping data science education broadly inclusive across sectors, institutions, and populations. Input was collected from data science programs across the nation, either traditional or alternative, and from a range of institution types including community colleges, minority-led and minority-serving institutions, liberal arts colleges, tribal colleges, universities, and industry partners. The series consisted of two webinars (August 2017 and September 2017) leading up to a workshop (November 2017) exploring the future of data science education and workforce at institutions of higher learning that are primarily teaching-focused. A third follow-up webinar was held after the workshop (January 2018) to report on outcomes and next steps. Program committee members were chosen to represent a broad spectrum of communities with a diversity of geography (West, Northeast, Midwest, and South), discipline (Computer Science, Math, Statistics, and Domains), as well as institution type (Historically Black Colleges and Universities (HBCU’s), Hispanic-Serving Institutions (HSI’s), other Minority-Serving Institutions (MSI\u27s), Community College\u27s (CC’s), 4-year colleges, Tribal Colleges, R1 Universities, Government and Industry Partners)

Keeping Data Science Broad: Negotiating the Digital and Data Divide Among Higher Education Institutions

The goal of the “Keeping Data Science Broad” series of webinars and workshops was to garner community input into pathways for keeping data science education broadly inclusive across sectors, institutions, and populations. Input was collected from data science programs across the nation, either traditional or alternative, and from a range of institution types including community colleges, minority-led and minority-serving institutions, liberal arts colleges, tribal colleges, universities, and industry partners. The series consisted of two webinars (August 2017 and September 2017) leading up to a workshop (November 2017) exploring the future of data science education and workforce at institutions of higher learning that are primarily teaching-focused. A third follow-up webinar was held after the workshop (January 2018) to report on outcomes and next steps. Program committee members were chosen to represent a broad spectrum of communities with a diversity of geography (West, Northeast, Midwest, and South), discipline (Computer Science, Math, Statistics, and Domains), as well as institution type (Historically Black Colleges and Universities (HBCU’s), Hispanic-Serving Institutions (HSI’s), other Minority-Serving Institutions (MSI\u27s), Community College\u27s (CC’s), 4-year colleges, Tribal Colleges, R1 Universities, Government and Industry Partners)

Mouse models of nesprin-related diseases

Nesprins (nuclear envelope spectrin repeat proteins) are a family of multi-isomeric scaffolding proteins. Nesprins form the LInker of Nucleoskeleton-and-Cytoskeleton (LINC) complex with SUN (Sad1p/UNC84) domain-containing proteins at the nuclear envelope, in association with lamin A/C and emerin, linking the nucleoskeleton to the cytoskeleton. The LINC complex serves as both a physical linker between the nuclear lamina and the cytoskeleton and a mechanosensor. The LINC complex has a broad range of functions and is involved in maintaining nuclear architecture, nuclear positioning and migration, and also modulating gene expression. Over 80 disease-related variants have been identified in SYNE-1/2 (nesprin-1/2) genes, which result in muscular or central nervous system disorders including autosomal dominant Emery–Dreifuss muscular dystrophy, dilated cardiomyopathy and autosomal recessive cerebellar ataxia type 1. To date, 17 different nesprin mouse lines have been established to mimic these nesprin-related human diseases, which have provided valuable insights into the roles of nesprin and its scaffold LINC complex in a tissue-specific manner. In this review, we summarise the existing nesprin mouse models, compare their phenotypes and discuss the potential mechanisms underlying nesprin-associated diseases

A National Survey of Hereditary Angioedema and Acquired C1 Inhibitor Deficiency in the United Kingdom

Background: Detailed demographic data on people with hereditary angioedema (HAE) and acquired C1 inhibitor deficiency in the United Kingdom are relatively limited. Better demographic data would be beneficial in planning service provision, identifying areas of improvement, and improving care./ Objective: To obtain more accurate data on the demographics of HAE and acquired C1 inhibitor deficiency in the United Kingdom, including treatment modalities and services available to patients./ Methods: A survey was distributed to all centers in the United Kingdom that look after patients with HAE and acquired C1 inhibitor deficiency to collect these data./ Results: The survey identified 1152 patients with HAE-1/2 (58% female and 92% type 1), 22 patients with HAE with normal C1 inhibitor, and 91 patients with acquired C1 inhibitor deficiency. Data were provided by 37 centers across the United Kingdom. This gives a minimum prevalence of 1:59,000 for HAE-1/2 and 1:734,000 for acquired C1 inhibitor deficiency in the United Kingdom. A total of 45% of patients with HAE were on long-term prophylaxis (LTP) with the most used medication being danazol (55% of all patients on LTP). Eighty-two percent of patients with HAE had a home supply of acute treatment with C1 inhibitor or icatibant. A total of 45% of patients had a supply of icatibant and 56% had a supply of C1 inhibitor at home./ Conclusions: Data obtained from the survey provide useful information about the demographics and treatment modalities used in HAE and acquired C1 inhibitor deficiency in the United Kingdom. These data are useful for planning service provision and improving services for these patients